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Generative Adversarial Network

Creator: Petar Veličković (original)

Generative adversarial network (GAN) architecture. A GAN has two parts. The discriminator DD acts as a classifier that learns to distinguish fake data produced by the generator GG from real data. GG incurs a penalty when DD detects implausible results. This signal is backpropagated through the generator weights such that GG learns to produce more realistic samples over time, eventually fooling the discriminator if training succeeds.


Generative Adversarial Network

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Code

generative-adversarial-network.typ (89 lines)

#import "@preview/cetz:0.5.2": canvas, draw
#import draw: circle, content, hobby, line, on-layer

#set page(width: auto, height: auto, margin: 8pt, fill: none)
#set text(size: 12pt)

#canvas({
  // Style definitions
  let node-style = (
    stroke: none,
    fill: rgb("#66B2B2"), // teal!60 equivalent
    radius: 0.53,
  )
  let arrow-style = (
    stroke: 0.8pt,
    mark: (end: "stealth", scale: 0.4, fill: black),
  )
  let annotated-arrow(start, end, name, formula, caption: none, caption-padding: 0.1) = {
    line(start, end, ..arrow-style, name: name)
    content(name + ".mid", formula, anchor: "south", padding: 0.1)
    if caption != none {
      content(
        name + ".mid",
        text(size: 14pt, caption),
        anchor: "north",
        padding: caption-padding,
      )
    }
  }

  let (y-real, y-fake) = (2, 0)

  for (pos, name, label, radius) in (
    ((0, y-fake), "zin", $arrow(z)_"in"$, 0.53),
    ((3, y-fake), "fake", $arrow(x)_"fake"$, 0.53),
    ((3, y-real), "real", $arrow(x)_"real"$, 0.53),
    ((6, y-real / 2), "D", $arrow(x)$, 0.4),
  ) {
    circle(pos, name: name, ..node-style, radius: radius)
    content(name, label)
  }

  // Output node
  content(
    (9, y-real / 2),
    text(size: 12pt, baseline: -1pt)[real?],
    name: "out",
    padding: 2pt,
  )

  annotated-arrow((-3.2, y-fake), "zin", "zin-line", $p_theta (arrow(z))$, caption: [latent noise])
  annotated-arrow("zin", "fake", "fake-line", $G(arrow(x))$, caption: [generator])
  annotated-arrow((-2, y-real), "real", "real-line", $p_"data" (arrow(x))$)

  // Connection points with names
  for (idx, y) in ((1, y-fake), (2, y-real)) {
    circle((4.5, y), radius: 0.06, fill: black, name: "dot" + str(idx))
  }
  on-layer(1, circle(
    (4.25, 2 * y-real / 3),
    radius: 0.12,
    fill: orange,
    stroke: none,
    name: "dot3",
  ))

  for (idx, start, end) in ((1, "fake", "dot1"), (2, "real", "dot2"), (3, "dot3", "D")) {
    line(start, end, ..arrow-style, name: "conn" + str(idx))
  }

  hobby(
    "dot1",
    (4.2, (y-real - y-fake) / 2),
    "dot2",
    stroke: (dash: "dashed"),
    omega: 2,
    name: "dashed-curve",
  )

  annotated-arrow(
    "D",
    "out",
    "disc-line",
    $D(arrow(x))$,
    caption: [discriminator],
    caption-padding: 0.45,
  )
})